Addressing climate change is among the most challenging ethical issues facing contemporary business and society. Unsustainable business activities are causing significant distributional and ...procedural injustices in areas such as public health and vulnerability to extreme weather events, primarily because of a distinction between primary emitters and those already experiencing the impacts of climate change. Business, as a significant contributor to climate change and beneficiary of externalizing environmental costs, has an obligation to address its environmental impacts. In this paper, we explore the role of firms' climate change targets in shaping their emissions trends in the context of a large multi-country sample of companies. We contrast two intentions for setting emissions reductions targets: symbolic attempts to manage external stakeholder perceptions via "greenwashing" and substantive commitments to reducing environmental impacts. We argue that the attributes of firms' climate change targets (their extent, form, and time horizon) are diagnostic of firms' underlying intentions. Consistent with our hypotheses, while we find no overall effect of setting climate change targets on emissions, we show that targets characterized by a commitment to more ambitious emissions reductions, a longer target time frame, and absolute reductions in emissions are associated with significant reductions in firms' emissions. Our evidence suggests the need for vigilance among policy-makers and environmental campaigners regarding the underlying intentions that accompany environmental management practices and shows that these can to some extent be diagnosed analytically.
Smartphones now offer the promise of collecting behavioral data unobtrusively, in situ, as it unfolds in the course of daily life. Data can be collected from the onboard sensors and other phone logs ...embedded in today's off-the-shelf smartphone devices. These data permit fine-grained, continuous collection of people's social interactions (e.g., speaking rates in conversation, size of social groups, calls, and text messages), daily activities (e.g., physical activity and sleep), and mobility patterns (e.g., frequency and duration of time spent at various locations). In this article, we have drawn on the lessons from the first wave of smartphone-sensing research to highlight areas of opportunity for psychological research, present practical considerations for designing smartphone studies, and discuss the ongoing methodological and ethical challenges associated with research in this domain. It is our hope that these practical guidelines will facilitate the use of smartphones as a behavioral observation tool in psychological science.
While the disruptive potential of artificial intelligence (AI) and big data has been receiving growing attention and concern in a variety of research and application fields over the last few years, ...it has not received much scrutiny in contemporary entrepreneurship research so far. Here we present some reflections and a collection of papers on the role of AI and big data for this emerging area in the study and application of entrepreneurship research. While being mindful of the potentially overwhelming nature of the rapid progress in machine intelligence and other big data technologies for contemporary structures in entrepreneurship research, we put an emphasis on the reciprocity of the coevolving fields of entrepreneurship research and practice. How can AI and big data contribute to a productive transformation of the research field and the real-world phenomena (e. g., “smart entrepreneurship”)? We also discuss, however, ethical issues as well as challenges around a potential contradiction between entrepreneurial uncertainty and rule-driven AI rationality. The editorial gives researchers and practitioners orientation and showcases avenues and examples for concrete research in this field. At the same time, however, it is not unlikely that we will encounter unforeseeable and currently inexplicable developments in the field soon. We call on entrepreneurship scholars, educators, and practitioners to proactively prepare for future scenarios.
Educational neuroscience is an interdisciplinary research field that seeks to translate research findings on neural mechanisms of learning to educational practice and policy and to understand the ...effects of education on the brain. Neuroscience and education can interact directly, by virtue of considering the brain as a biological organ that needs to be in the optimal condition to learn (‘brain health’); or indirectly, as neuroscience shapes psychological theory and psychology influences education. In this article, we trace the origins of educational neuroscience, its main areas of research activity and the principal challenges it faces as a translational field. We consider how a pure psychology approach that ignores neuroscience is at risk of being misleading for educators. We address the major criticisms of the field comprising, respectively, a priori arguments against the relevance of neuroscience to education, reservations with the current practical operation of the field, and doubts about the viability of neuroscience methods for diagnosing disorders or predicting individual differences. We consider future prospects of the field and ethical issues it raises. Finally, we discuss the challenge of responding to the (welcome) desire of education policymakers to include neuroscience evidence in their policymaking, while ensuring recommendations do not exceed the limitations of current basic science.
Read the Commentary on this article at doi: 10.1111/jcpp.13030
Recommendations by health experts to deal with public health emergencies are primarily guided by the principle of “saving more lives”. It is unclear whether people perceive this principle as ...ethically more legitimate than some other principle such as “saving more life-years”. Understanding the answer to this question is particularly relevant to the allocation of scarce medical resources during public health emergencies. Different principles typically lead to different allocations, and consequently have dramatically different implications as to who survives and who dies. We fielded an online randomized controlled survey experiment in the context of scarce ventilator allocation with a demographically representative sample of US adults (n = 700) from October 22 to October 30, 2020. Participants faced hypothetical situations where they had to allocate few available ventilators among several needy patients. The experiment was designed such that the allocation decision made by a participant can be used to infer the principle in line with their personal ethical values. We interpret this inferred principle as the one that the participant perceives to be most legitimate. The treatment group, but not the control group, was provided balanced information that described the ethical dilemmas faced by experts in developing ventilator allocation guidelines. Nearly half of the participants in the control group perceive saving more lives the most legitimate principle. Despite the balanced nature of the information, the perceived legitimacy of saving more lives was 7·6 percentage points higher in the treatment group. The magnitude of this impact was particularly strong among republican-leaning participants, a subgroup that has less trust in experts according to previous research. Our findings suggest that enhancing public awareness of ethical dilemmas faced by health experts can increase the perceived legitimacy of their proposed guidelines even among those with lower trust in experts.
•The principle of saving more lives (SML) guides public health recommendations.•It is unclear if people perceive SML as ethically legitimate.•We conducted a survey experiment in the context of scarce ventilator allocation.•Nearly half of control participants perceive SML the most legitimate principle.•Awareness of the underlying ethical dilemmas increases perceived legitimacy of SML.
To better understand the consequences of ethical voice in organizations, we have brought together multiple relevant literatures that focus on behaviors that fit our definition of ethical voice but ...have previously not been studied together, including internal reporting, social issue selling, ethical voice (in groups), moral objection, and confronting prejudice. Research across them has found both positive and negative responses to ethical voice. Further, emerging evidence suggests ambivalent attitudes and emotions toward ethical voice and voicers, hinting at more complex outcomes. However, a systematic understanding of when and why positive, negative, and more complex outcomes occur has remained elusive and is much needed. Building on empirical evidence, theory and research on ethical decision-making, self-enhancement/protection, and ambivalence, we offer an integrative theoretical framework to understand when and why ethical voice leads to targets'/observers' support for, undermining of, and inaction/disengagement from ethical voice and the voicer. We propose a morally motivated process, an instrumentally motivated process, and emotional ambivalence to explain these different responses. We also propose boundary conditions. We discuss our contributions and propose future directions for ethical voice research.
Ethical dilemmas arise when one must decide between conflicting ethical imperatives. One potential ethical dilemma is a manager's decision of whether to engage in corporate social responsibility ...(CSR) activities. This decision could pit the ethical imperative of honoring unwritten obligations to society against the ethical imperative of honoring contractual obligations to the firm. However, CSR activities might only be a minor ethical dilemma or none at all if they simultaneously benefit the firm and society. To examine this I test the association between future-period employee productivity and current-period use of one type of CSR activity: employee volunteer programs. I use a unique sample of 1428 firm-years, hand-collected from sustainability reports of 373 firms. I find evidence that the current-period use of an employee volunteer program has a positive association with future-period employee productivity (moderated by the firm's current-period employee productivity). I find this result in future periods up to 6 years after a firm uses an employee volunteer program. I also find a positive association between incentives that focus CEOs' attention on longterm firm outcomes and more extensive employee volunteer programs (also moderated by current-period employee productivity).
Research in embodied artificial intelligence (AI) has increasing clinical relevance for therapeutic applications in mental health services. With innovations ranging from 'virtual psychotherapists' to ...social robots in dementia care and autism disorder, to robots for sexual disorders, artificially intelligent virtual and robotic agents are increasingly taking on high-level therapeutic interventions that used to be offered exclusively by highly trained, skilled health professionals. In order to enable responsible clinical implementation, ethical and social implications of the increasing use of embodied AI in mental health need to be identified and addressed.
This paper assesses the ethical and social implications of translating embodied AI applications into mental health care across the fields of Psychiatry, Psychology and Psychotherapy. Building on this analysis, it develops a set of preliminary recommendations on how to address ethical and social challenges in current and future applications of embodied AI.
Based on a thematic literature search and established principles of medical ethics, an analysis of the ethical and social aspects of currently embodied AI applications was conducted across the fields of Psychiatry, Psychology, and Psychotherapy. To enable a comprehensive evaluation, the analysis was structured around the following three steps: assessment of potential benefits; analysis of overarching ethical issues and concerns; discussion of specific ethical and social issues of the interventions.
From an ethical perspective, important benefits of embodied AI applications in mental health include new modes of treatment, opportunities to engage hard-to-reach populations, better patient response, and freeing up time for physicians. Overarching ethical issues and concerns include: harm prevention and various questions of data ethics; a lack of guidance on development of AI applications, their clinical integration and training of health professionals; 'gaps' in ethical and regulatory frameworks; the potential for misuse including using the technologies to replace established services, thereby potentially exacerbating existing health inequalities. Specific challenges identified and discussed in the application of embodied AI include: matters of risk-assessment, referrals, and supervision; the need to respect and protect patient autonomy; the role of non-human therapy; transparency in the use of algorithms; and specific concerns regarding long-term effects of these applications on understandings of illness and the human condition.
We argue that embodied AI is a promising approach across the field of mental health; however, further research is needed to address the broader ethical and societal concerns of these technologies to negotiate best research and medical practices in innovative mental health care. We conclude by indicating areas of future research and developing recommendations for high-priority areas in need of concrete ethical guidance.
For decades, our ability to predict suicide has remained at near‐chance levels. Machine learning has recently emerged as a promising tool for advancing suicide science, particularly in the domain of ...suicide prediction. The present review provides an introduction to machine learning and its potential application to open questions in suicide research. Although only a few studies have implemented machine learning for suicide prediction, results to date indicate considerable improvement in accuracy and positive predictive value. Potential barriers to algorithm integration into clinical practice are discussed, as well as attendant ethical issues. Overall, machine learning approaches hold promise for accurate, scalable, and effective suicide risk detection; however, many critical questions and issues remain unexplored.
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